--- title: "Why We Rebuilt LangChain’s Chatbot and What We Learned" url: https://daily.dev/posts/why-we-rebuilt-langchain-s-chatbot-and-what-we-learned-pnjgfiljk source_url: https://www.langchain.com/blog/rebuilding-chat-langchain type: article source: "LangChain" published: 2026-08-26T16:39:01.986Z updated: 2026-08-26T16:40:42.649Z tags: ["ai-agents", "rag", "vector-search", "langchain", "langsmith"] reading_time: 15 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Why We Rebuilt LangChain’s Chatbot and What We Learned **[LangChain](https://daily.dev/sources/langchain)** · 15 min read · 0 upvotes · 0 comments ## Summary LangChain's team explains why they rebuilt their public chat.langchain.com support chatbot after discovering their own engineers avoided using it in favor of a manual three-step workflow: checking docs, the knowledge base, and the codebase. Instead of chunking documents into embeddings and storing them in a vector database, they moved to direct API access (via Mintlify for docs, Pylon for knowledge base, and ripgrep for codebase search), giving agents full-page context instead of fragments. They built a fast Create Agent (using Claude Haiku 4.5) for simple docs Q&A delivering sub-15-second responses, and a slower Deep Agent with specialized subgraphs for complex code-related queries taking 1-3 minutes. Production middleware handles guardrails, retries, model fallback, and caching. The piece closes with key takeaways on when vector embeddings help versus hurt and how subgraphs prevent context overload. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why might vector embeddings and chunking be a bad fit for searching structured product documentation? Chunking breaks the structure of documentation by splitting it into small fragments (e.g. 500-token pieces), which loses headers, subsections, and context needed to explain answers properly. It also requires constant reindexing whenever docs update, and produces vague citations that users cannot trace back to a source. Vector embeddings work better for unstructured content like PDFs. _Anyone weighing RAG architecture choices can find real-world tradeoffs like this one on daily.dev._ ### What is the difference between LangChain's Create Agent and Deep Agent architectures for a support chatbot? Create Agent is a fast, no-planning-overhead agent that makes 3-6 tool calls and answers documentation questions in under 15 seconds, using models like Claude Haiku 4.5. Deep Agent uses specialized subgraphs (docs, knowledge base, codebase) that each filter results before passing them to an orchestrator, taking 1-3 minutes but handling complex queries requiring code-level verification. _Developers comparing agent architectures for speed versus depth can track patterns like this on daily.dev._ ### How does a multi-agent subgraph design prevent context overload in an LLM orchestrator? Each specialized subagent (for documentation, knowledge base, and codebase) searches its own domain independently, filters through raw results, and passes only the essential extracted facts and citations to the main orchestrator agent, which never sees the raw search results. This avoids dumping dozens of full documents and code snippets into one context window, keeping responses focused instead of bloated or unfocused. _Engineers designing multi-agent systems can follow architecture lessons like these on daily.dev._ ## Similar posts on daily.dev - [Why We Rebuilt LangChain’s Chatbot and What We Learned](https://daily.dev/posts/why-we-rebuilt-langchain-s-chatbot-and-what-we-learned-h1oubdlqy) · LangChain · 9 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#rag](https://daily.dev/tags/rag), [#vector-search](https://daily.dev/tags/vector-search), [#langchain](https://daily.dev/tags/langchain), [#langsmith](https://daily.dev/tags/langsmith) [View this post on daily.dev](https://daily.dev/posts/why-we-rebuilt-langchain-s-chatbot-and-what-we-learned-pnjgfiljk)